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A hybrid Genetic Algorithm approach to minimize the total joint cost of a single-vendor multi-customer integrated scheduling problem

Author

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  • Olivier Grunder
  • Zakaria Hammoudan
  • Benoit Beroule
  • Oussama Barakat

Abstract

This paper addresses the scheduling of supply chains with interrelated factories consisting of a single vendor and multiple customers. In this research, one transporter is available to deliver jobs from vendor to customers, and the jobs can be processed by batch. The problem studied in this paper focuses on a real-case scheduling problem of a multi-location hospital supplied with a central pharmacy. The objective of this work is to minimize the total cost, while satisfying the customer’s due dates constraints. A mathematical formulation of the problem is given as a Mixed Integer Programming model. Then, a Branch-and-Bound algorithm is proposed as an exact method for solving this problem, a greedy local search is developed as a heuristic approach, and a hybrid Genetic Algorithm is presented as a meta-heuristic. Computation experiments are conducted to highlight the performance of the proposed methods.

Suggested Citation

  • Olivier Grunder & Zakaria Hammoudan & Benoit Beroule & Oussama Barakat, 2019. "A hybrid Genetic Algorithm approach to minimize the total joint cost of a single-vendor multi-customer integrated scheduling problem," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(6), pages 625-642, August.
    • Handle: RePEc:taf:transp:v:42:y:2019:i:6:p:625-642
    DOI: 10.1080/03081060.2019.1622254
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